diff --git a/astra/torch/al/acquisitions/max_entropy.py b/astra/torch/al/acquisitions/max_entropy.py
new file mode 100644
index 0000000..7f86a8f
--- /dev/null
+++ b/astra/torch/al/acquisitions/max_entropy.py
@@ -0,0 +1,16 @@
+import torch 
+from base import EnsembleAcquisition, MCAcquisition
+
+class MaxEntropy(EnsembleAcquisition, MCAcquisition):
+    def acquire_scores(self, logits: torch.Tensor):
+        
+        #calculate entropy for each pool datapoint for each model
+        probs=torch.softmax(logits,dim=2)
+        entropy=-torch.sum(probs*torch.log(probs),dim=2)
+
+
+        score=torch.sum(entropy,dim=0)
+
+        return score
+
+      
\ No newline at end of file
diff --git a/tests/torch/acquisitions/test_max_entropy.ipynb b/tests/torch/acquisitions/test_max_entropy.ipynb
new file mode 100644
index 0000000..f5ae46d
--- /dev/null
+++ b/tests/torch/acquisitions/test_max_entropy.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ensemble Acquisition Scores:\n",
+      "tensor([2.0118, 2.0402, 1.9574])\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from astra.torch.al.acquisitions.base import MCAcquisition, EnsembleAcquisition\n",
+    "\n",
+    "\n",
+    "class MaxEntropyAcquisition(MCAcquisition, EnsembleAcquisition):\n",
+    "    def acquire_scores(self, logits: torch.Tensor):\n",
+    "        probs = torch.softmax(logits, dim=2)\n",
+    "        entropy = -torch.sum(probs * torch.log(probs), dim=2)\n",
+    "        score = torch.sum(entropy, dim=0)\n",
+    "        return score\n",
+    "\n",
+    "\n",
+    "# Create an instance of MaxEntropyAcquisition\n",
+    "max_entropy_acquisition = MaxEntropyAcquisition()\n",
+    "\n",
+    "logits = torch.tensor(\n",
+    "    [\n",
+    "        [[0.2, 0.8], [0.7, 0.3], [0.4, 0.6]],\n",
+    "        [[0.6, 0.4], [0.3, 0.7], [0.8, 0.2]],\n",
+    "        [[0.3, 0.7], [0.5, 0.5], [0.9, 0.1]],\n",
+    "    ],\n",
+    "    dtype=torch.float32,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Calculate acquisition scores using the ensemble context\n",
+    "ensemble_scores = max_entropy_acquisition.acquire_scores(logits)\n",
+    "\n",
+    "# Calculate acquisition scores using the Monte Carlo context\n",
+    "mc_scores = max_entropy_acquisition.acquire_scores(mc_logits)\n",
+    "\n",
+    "# Print the results\n",
+    "print(\"Ensemble Acquisition Scores:\")\n",
+    "print(ensemble_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "torch_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}